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Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

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  • Pan, Jeng-Shyang
  • Zhang, Li-Gang
  • Wang, Ruo-Bin
  • Snášel, Václav
  • Chu, Shu-Chuan

Abstract

Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA’s U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.

Suggested Citation

  • Pan, Jeng-Shyang & Zhang, Li-Gang & Wang, Ruo-Bin & Snášel, Václav & Chu, Shu-Chuan, 2022. "Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 343-373.
    • Handle: RePEc:eee:matcom:v:202:y:2022:i:c:p:343-373
    DOI: 10.1016/j.matcom.2022.06.007
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    References listed on IDEAS

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    1. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
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    Cited by:

    1. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    2. Jeng-Shyang Pan & Bing Sun & Shu-Chuan Chu & Minghui Zhu & Chin-Shiuh Shieh, 2023. "A Parallel Compact Gannet Optimization Algorithm for Solving Engineering Optimization Problems," Mathematics, MDPI, vol. 11(2), pages 1-23, January.
    3. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    4. Jeng-Shyang Pan & Li-Fa Liu & Shu-Chuan Chu & Pei-Cheng Song & Geng-Geng Liu, 2023. "A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
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    6. Pan, Jeng-Shyang & Zhang, Zhen & Chu, Shu-Chuan & Zhang, Si-Qi & Wu, Jimmy Ming-Tai, 2024. "A parallel compact Marine Predators Algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 65-88.

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